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BirdSLAM: Monocular multibody SLAM in bird’s-eye view 16

BirdSLAM: Monocular multibody SLAM in bird’s-eye view

16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021

作者： Daga, Swapnil Nair, Gokul B. Ramesh, Anirudha Sajnani, Rahul Ansari, Junaid Ahmed Madhava Krishna, K. Robotics Research Center KCIS IIIT Hyderabad India Embedded Systems and Robotics TCS Innovation Labs Kolkata India

ISBN: (纸本)9789897584886

In this paper, we present BirdSLAM, a novel simultaneous localization and mapping (SLAM) system for the challenging scenario of autonomous driving platforms equipped with only a monocular camera. BirdSLAM tackles challenges faced by other monocular SLAM systems (such as scale ambiguity in monocular reconstruction, dynamic object localization, and uncertainty in feature representation) by using an orthographic (bird’s-eye) view as the configuration space in which localization and mapping are performed. By assuming only the height of the ego-camera above the ground, BirdSLAM leverages single-view metrology cues to accurately localize the ego-vehicle and all other traffic participants in bird’s-eye view. We demonstrate that our system outperforms prior work that uses strictly greater information, and highlight the relevance of each design decision via an ablation analysis. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Birds

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Multi-Agent Reinforcement Learning for Cooperative Vehicle Motion Control

Multi-Agent Reinforcement Learning for Cooperative Vehicle M...

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International Conference on Intelligent Transportation

作者： Kenan Ahmic Johannes Ultsch Jonathan Brembeck Darius Burschka Institute of System Dynamics and Control at the German Aerospace Center (DLR) Wessling Germany Chair of Robotics Artificial Intelligence and Embedded Systems Technical University of Munich (TUM) Munich Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The longitudinal and lateral low-level motion control of multiple vehicles within a platoon is a challenging task, since several different control objectives need to be solved: (i) Each vehicle in the platoon needs to follow the reference path, (ii) the leading vehicle needs to drive with a desired reference velocity, and (iii) the following vehicles need to maintain a safe spacing distance to their respective preceding vehicle. Typically, several distinct controllers are developed for each task individually, which increases both the engineering effort as well as the susceptibility to errors. We address this issue and present a cooperative low-level vehicle motion controller based on Multi-Agent Reinforcement Learning (MARL) that is able to solve all of the above-mentioned control objectives for both the leading vehicle and the following vehicles. Therefore, we apply parameter sharing within MARL to update a single control policy in a centralized fashion using the experiences of all vehicles in the environment. Additionally, we utilize the concept of agent indication during the training process and enable the policy to specialize on the control objectives of the vehicle it is currently controlling. This leads to a unifying control approach and makes the development of further controllers redundant. The simulative assessment demonstrates the effectiveness of learned policy and shows that it is able to successfully solve all of the above-mentioned control objectives for both vehicles roles, even on unseen paths.

关键词： Training Process control Reinforcement learning Drives Vectors Robustness Collision avoidance Intelligent transportation systems

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Multi-object Monocular SLAM for Dynamic Environments 31

Multi-object Monocular SLAM for Dynamic Environments

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31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Nair, Gokul B. Daga, Swapnil Sajnani, Rahul Ramesh, Anirudha Ansari, Junaid Ahmed Jatavallabhula, Krishna Murthy Krishna, K. Madhava Robotics Research Center Kcis Iiit Hyderabad India Embedded Systems and Robotics Tcs Innovation Labs Kolkata India Mila Universite de Montreal Canada

In this paper, we tackle the problem of multibody SLAM from a monocular camera. The term multibody, implies that we track the motion of the camera, as well as that of other dynamic participants in the scene. The quintessential challenge in dynamic scenes is unobservability: it is not possible to unambiguously triangulate a moving object from a moving monocular camera. Existing approaches solve restricted variants of the problem, but the solutions suffer relative scale ambiguity (i.e., a family of infinitely many solutions exist for each pair of motions in the scene). We solve this rather intractable problem by leveraging single-view metrology, advances in deep learning, and category-level shape estimation. We propose a multi pose-graph optimization formulation, to resolve the relative and absolute scale factor ambiguities involved. This optimization helps us reduce the average error in trajectories of multiple bodies over real-world datasets, such as KITTI [1]. To the best of our knowledge, our method is the first practical monocular multi-body SLAM system to perform dynamic multi-object and ego localization in a unified framework in metric scale. © 2020 IEEE.

关键词： Cameras

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BirdSLAM: Monocular multibody SLAM in bird’s-eye view

arXiv

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arXiv 2020年

作者： Daga, Swapnil Nair, Gokul B. Ramesh, Anirudha Sajnani, Rahul Ansari, Junaid Ahmed Krishna, K. Madhava Robotics Research Center KCIS IIIT Hyderabad India Embedded Systems and Robotics TCS Innovation Labs Kolkata India

关键词： Birds

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Multi-object monocular slam for dynamic environments

arXiv

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arXiv 2020年

作者： Nair, Gokul B. Daga, Swapnil Sajnani, Rahul Ramesh, Anirudha Ansari, Junaid Ahmed Krishna Murthy, J. Madhava Krishna, K. Robotics Research Center KCIS IIIT Hyderabad India Embedded Systems and Robotics TCS Innovation Labs Kolkata India Mila Universite de Montreal Canada

Multibody monocular SLAM in dynamic environments remains a long-standing challenge in terms of perception and state estimation. Although theoretical solutions exist, practice lags behind, predominantly due to the lack of robust perceptual and predictive models of dynamic participants. The quintessential challenge in Multi-body monocular SLAM in dynamic scenes stems from the problem of unobservability as it is not possible to triangulate a moving object from a moving monocular camera. Under restrictions of object motion the problem can be solved, however even here one is entailed to solve for the single family solution to the relative scale problem. The relative scale problem exists since the dynamic objects that get reconstructed with the monocular camera have a different scale vis a vis the scale space in which the stationary scene is reconstructed. We solve this rather intractable problem by reconstructing dynamic vehicles/participants in single view in metric scale through an object SLAM pipeline. Further, we lift the ego vehicle trajectory obtained from Monocular ORB-SLAM also into metric scales making use of ground plane features thereby resolving the relative scale problem. We present a multi pose-graph optimization formulation to estimate the pose and track dynamic objects in the environment. This optimization helps us reduce the average error in trajectories of multiple bodies in KITTI Tracking sequences. To the best of our knowledge, our method is the first practical monocular multi-body SLAM system to perform dynamic multi-object and ego localization in a unified framework in metric scale. Copyright © 2020, The Authors. All rights reserved.

关键词： Vision

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Multi-object Monocular SLAM for Dynamic Environments

Multi-object Monocular SLAM for Dynamic Environments

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IEEE Symposium on Intelligent Vehicle

作者： Gokul B. Nair Swapnil Daga Rahul Sajnani Anirudha Ramesh Junaid Ahmed Ansari Krishna Murthy Jatavallabhula K. Madhava Krishna Robotics Research Center KCIS IIIT Hyderabad India Embedded Systems and Robotics TCS Innovation Labs Kolkata India Mila Universite de Montreal Canada

ISBN: (数字)9781728166735

ISBN: (纸本)9781728166742

关键词： Simultaneous localization and mapping Vehicle dynamics Cameras Dynamics Shape Measurement Trajectory

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Correction to: Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

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Multimedia Tools and Applications 2022年第6期82卷 9551-9552页

作者： Garg, Gaurav Prasad, Girijesh Garg, Lalit Miyakoshi, Makoto Nakai, Toshiharu Coyle, Damien Embedded & Robotics BrainAlive Research Pvt. Ltd Kanpur India School of Computing and Intelligent Systems Magee Campus Ulster University Londonderry UK Faculty of Information & Communication Technology University of Malta Msida Malta Swartz Center for Computational Neuroscience Institute for Neural Computation University of California San Diego San Diego USA Neuroimaging & Informatics Lab (Niinf) National Centre for Geriatrics and Gerontology (NCGG) Obu Japan

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Learning to Prevent Monocular SLAM Failure using Reinforcement Learning 18

Learning to Prevent Monocular SLAM Failure using Reinforceme...

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Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing

作者： Vignesh Prasad Karmesh Yadav Rohitashva Singh Saurabh Swapnil Daga Nahas Pareekutty K. Madhava Krishna Balaraman Ravindran Brojeshwar Bhowmick Embedded Systems & Robotics TCS Research & Innovation Kolkata India Robotics Institute Carnegie Mellon University Pittsburgh USA Dept. of Robotics Engineering John Hopkins University Baltimore USA Robotics Research Center KCIS IIIT Hyderabad India Dept. of Computer Science Indian Institute of Technology Madras India

ISBN: (纸本)9781450366151

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

关键词： Monocular SLAM

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Overtaking Maneuvers in Simulated Highway Driving using Deep Reinforcement Learning

Overtaking Maneuvers in Simulated Highway Driving using Deep...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Kaushik, Meha Prasad, Vignesh Krishna, K. Madhava Ravindran, Balaraman Robotics Research Center KCIS IIIT Hyderabad India Embedded Systems and Robotics TCS Innovation Labs Kolkata India Dept. of Computer Science IIT Madras India

ISBN: (纸本)9781538644522

Most methods that attempt to tackle the problem of Autonomous Driving and overtaking usually try to either directly minimize an objective function or iteratively in a Reinforcement Learning like framework to generate motor actions given a set of inputs. We follow a similar trend but train the agent in a way similar to a curriculum learning approach where the agent is first given an easier problem to solve, followed by a harder problem. We use Deep Deterministic Policy Gradients to learn overtaking maneuvers for a car, in presence of multiple other cars, in a simulated highway scenario. The novelty of our approach lies in the training strategy used where we teach the agent to drive in a manner similar to the way humans learn to drive and the fact that our reward function uses only the raw sensor data at the current time step. This method, which resembles a curriculum learning approach is able to learn smooth maneuvers, largely collision free, wherein the agent overtakes all other cars, independent of the track and number of cars in the scene. © 2018 IEEE.

关键词： Iterative methods

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Overtaking Maneuvers in Simulated Highway Driving using Deep Reinforcement Learning

Overtaking Maneuvers in Simulated Highway Driving using Deep...

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IEEE Symposium on Intelligent Vehicle

作者： Meha Kaushik Vignesh Prasad K Madhava Krishna Balaraman Ravindran Robotics Research Center KCIS IIIT Hyderabad Embedded Systems and Robotics TCS Innovation Labs Kolkata Indian Institute of Technology Madras Chennai Tamil Nadu IN

ISBN: (纸本)9781538644539

关键词： Automobiles Autonomous vehicles Training Robot sensing systems Tracking Task analysis Neural networks

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